US20180245154A1 - Methods to diagnose and treat acute respiratory infections - Google Patents

Methods to diagnose and treat acute respiratory infections Download PDF

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US20180245154A1
US20180245154A1 US15/738,339 US201615738339A US2018245154A1 US 20180245154 A1 US20180245154 A1 US 20180245154A1 US 201615738339 A US201615738339 A US 201615738339A US 2018245154 A1 US2018245154 A1 US 2018245154A1
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ari
bacterial
viral
infectious
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Ephraim L. Tsalik
Ricardo Henao Giraldo
Thomas W. Burke
Geoffrey S. Ginsburg
Christopher W. Woods
Micah T. McClain
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Duke University
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    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C12Q1/6876—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
    • C12Q1/6883—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61K—PREPARATIONS FOR MEDICAL, DENTAL OR TOILETRY PURPOSES
    • A61K31/00—Medicinal preparations containing organic active ingredients
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61K—PREPARATIONS FOR MEDICAL, DENTAL OR TOILETRY PURPOSES
    • A61K39/00—Medicinal preparations containing antigens or antibodies
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61P—SPECIFIC THERAPEUTIC ACTIVITY OF CHEMICAL COMPOUNDS OR MEDICINAL PREPARATIONS
    • A61P11/00—Drugs for disorders of the respiratory system
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61P—SPECIFIC THERAPEUTIC ACTIVITY OF CHEMICAL COMPOUNDS OR MEDICINAL PREPARATIONS
    • A61P31/00—Antiinfectives, i.e. antibiotics, antiseptics, chemotherapeutics
    • A61P31/04—Antibacterial agents
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61P—SPECIFIC THERAPEUTIC ACTIVITY OF CHEMICAL COMPOUNDS OR MEDICINAL PREPARATIONS
    • A61P31/00—Antiinfectives, i.e. antibiotics, antiseptics, chemotherapeutics
    • A61P31/12—Antivirals
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61P—SPECIFIC THERAPEUTIC ACTIVITY OF CHEMICAL COMPOUNDS OR MEDICINAL PREPARATIONS
    • A61P37/00—Drugs for immunological or allergic disorders
    • A61P37/02—Immunomodulators
    • A61P37/04—Immunostimulants
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/106—Pharmacogenomics, i.e. genetic variability in individual responses to drugs and drug metabolism
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/118—Prognosis of disease development
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/158—Expression markers
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
    • Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
    • Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation

Definitions

  • Acute respiratory infection is common in acute care environments and results in significant mortality, morbidity, and economic losses worldwide.
  • Respiratory tract infections, or acute respiratory infections (ARI) caused 3.2 million deaths around the world and 164 million disability-adjusted life years lost in 2011, more than any other cause (World Health Organization., 2013a, 2013b).
  • ARI acute respiratory infections
  • the fourth leading cause of death worldwide was lower respiratory tract infections, and in low and middle income countries, where less supportive care is available, lower respiratory tract infections are the leading cause of death (WHO factsheet, accessed August 22, 2014).
  • WHO factsheet accessed August 22, 2014.
  • These illnesses are also problematic in developed countries.
  • the Centers for Disease Control (CDC) determined that pneumonia and influenza alone caused 15.1 deaths for every 100,000 people in the US population.
  • the aged and children under the age of 5 years are particularly vulnerable to poor outcomes due to ARIs.
  • pneumonia accounted for 18.3% of all deaths, or almost 1.4 million deaths, worldwide in children aged 5 years or younger.
  • Pneumonia and other lower respiratory tract infections can be due to many different pathogens that are primarily viral, bacterial, or less frequently fungal.
  • influenza is among the most notorious based on numbers of affected individuals, variable severity from season to season, and the ever-present worry about new strains causing much higher morbidity and mortality (e.g., Avian flu).
  • Avian flu e.g., Avian flu
  • influenza is only one of many that cause significant human disease.
  • Respiratory Syncytial Virus (RSV) is the leading cause of hospitalization of children in developed countries during the winter months. Worldwide, about 33 million new cases of RSV infections were reported in 2005 in children under 5, with 3.4 million severe enough for hospitalization. It is estimated that this viral infection alone kills between 66,000 and 199,000 children each year.
  • bacterial etiologies are also prominent especially in the context of lower respiratory tract infections.
  • Specific causes of bacterial ARI vary geographically and by clinical context but include Streptococcus pneumoniae, Staphylococcus aureus, Haemophilus influenzae, Chlamydia pneumoniae, Mycoplasma pneumoniae, Klebsiella pneumoniae, Escherichia coli , and Pseudomonas aeruginosa .
  • Streptococcus pneumoniae Staphylococcus aureus
  • Haemophilus influenzae Chlamydia pneumoniae
  • Mycoplasma pneumoniae Mycoplasma pneumoniae
  • Klebsiella pneumoniae Klebsiella pneumoniae
  • Escherichia coli Escherichia coli
  • Pseudomonas aeruginosa The identification of these pathogens relies on their growth in culture, which typically requires days and has limited sensitivity for detection of the infectious agent.
  • Acute respiratory infections are frequently characterized by non-specific symptoms (such as fever or cough) that are common to many different illnesses, including illnesses that are not caused by an infection.
  • Existing diagnostics for ARI fall short in a number of ways.
  • Conventional microbiological testing is limited by poor sensitivity and specificity, slow turn-around times, or by the complexity of the test (Zaas et al. 2014, Trends Mol Med 20(10):579-88).
  • One limitation of current tests that detect specific viral pathogens for example the multiplex PCR-based assays, is the inability to detect emergent or pandemic viral strains. Influenza pandemics arise when new viruses circulate against which populations have no natural resistance. Influenza pandemics are frequently devastating.
  • the Spanish flu affected about 20% to 40% of the world's population and killed about 50 million people; in 1957-1958, Asian flu killed about 2 million people; in 1968-1969 the Hong Kong flu killed about 1 million people; and in 2009-2010, the Centers for Disease Control estimates that approximately 43 million to 89 million people contracted swine flu resulting in 8,870 to 18,300 related deaths.
  • the emergence of these new strains challenges existing diagnostics which are not designed to detect them. This was particularly evident during the 2009 influenza pandemic where confirmation of infection required days and only occurred at specialized testing centers such as state health departments or the CDC (Kumar & Henrickson 2012, Clin Microbiol Rev 25(2):344-61).
  • the Ebola virus disease outbreak in West Africa poses similar challenges at the present time. Moreover, there is every expectation we will continue to face this issue as future outbreaks of infectious diseases are inevitable.
  • a further limitation of diagnostics that use the paradigm of testing for specific viruses or bacteria is that even though a pathogenic microbe may be detected, this is not proof that the patient's symptoms are due to the detected pathogen.
  • a microorganism may be present as part of the individual's normal flora, known as colonization, or it may be detected due to contamination of the tested sample (e.g., a nasal swab or wash).
  • recently-approved multiplex PCR assays including those that detect viruses and bacteria, offer high sensitivity, these tests do not differentiate between asymptomatic carriage of a virus and true infection. For example, there is a high rate of asymptomatic viral shedding in ARI, particularly in children (Jansen et al. 2011, J Clin Microbiol 49(7):2631-2636). Similarly, even though one pathogen is detected, illness may be due to a second pathogen for which there was no test available or performed.
  • the present disclosure provides, in part, a molecular diagnostic test that overcomes many of the limitations of current methods for the determination of the etiology of respiratory symptoms.
  • the test detects the host's response to an infectious agent or agents by measuring and analyzing the patterns of co-expressed genes, or signatures. These gene expression signatures may be measured in a blood sample in a human or animal presenting with symptoms that are consistent with an acute respiratory infection or in a human or animal that is at risk of developing (e.g., presymptomatic) an acute respiratory infection (e.g., during an epidemic or local disease outbreak).
  • Measurement of the host response as taught herein differentiates between bacterial ARI, viral ARI, and a non-infectious cause of illness, and may also detect ARI resulting from co-infection with bacteria and virus.
  • This multi-component test performs with unprecedented accuracy and clinical applicability, allowing health care providers to use the response of the host (the subject or patient) to reliably determine the nature of the infectious agent, to the level of pathogen class, or to exclude an infectious cause of symptoms in an individual patient presenting with symptoms that, by themselves, are not specific.
  • the results are agnostic to the species of respiratory virus or bacteria (i.e., while differentiating between virus or bacteria, it does not differentiate between particular genus or species of virus or bacteria). This offers an advantage over current tests that include probes or reagents directed to specific pathogens and thus are limited to detecting only those specific pathogens.
  • One aspect of the present disclosure provides a method for determining whether acute respiratory symptoms in a subject are bacterial in origin, viral in origin, or non-infectious in origin comprising, consisting of, or consisting essentially of: (a) obtaining a biological sample from the subject; (b) determining the gene expression profile of the subject from the biological sample by evaluating the expression levels of pre-defined sets of genes, termed signatures; (c) normalizing gene expression levels for the technology (i.e., platform) used to make said measurement to generate a normalized value; (d) entering the normalized values into a bacterial classifier, a viral classifier and/or a non-infectious illness classifier that have pre-defined weighting values (coefficients) for each of the genes in each signature; (e) comparing the output of the classifiers to pre-defined thresholds, cut-off values, or ranges of values that indicate likelihood of infection; and (f) using the output to determine whether the patient providing the sample has an infection of bacterial origin, viral origin, or has a
  • Another aspect of the present disclosure provides a method for determining whether an acute respiratory infection (ARI) in a subject is bacterial in origin, viral in origin, or non-infectious in origin comprising, consisting of, or consisting essentially of: (a) obtaining a biological sample from the subject; (b) determining the gene expression profile of the subject from the biological sample by evaluating the expression levels of pre-defined sets of genes; (c) normalizing gene expression levels for the technology (i.e., platform) used to make said measurement to generate a normalized value; (d) entering the normalized value into classifiers that have pre-defined weighting values for each of the genes in each signature; e) comparing the output of the classifiers to pre-defined thresholds, cut-off values, or ranges of values that indicate likelihood of infection; (f) if the sample is negative for bacteria, repeating step (d) using only the viral classifier and non-infectious classifier; and (g) classifying the sample as being of viral etiology or noninfectious illness.
  • Another aspect of the present disclosure provides a method for determining whether an acute respiratory infection (ARI) in a subject is bacterial in origin, viral in origin, or non-infectious in origin comprising, consisting of, or consisting essentially of: (a) obtaining a biological sample from the subject; (b) determining the gene expression profile of the subject from the biological sample by evaluating the expression levels of pre-defined sets of genes; (c) normalizing gene expression levels for the technology (i.e., platform) used to make said measurement to generate a normalized value; (d) entering the normalized values into classifiers that have pre-defined weighting values for each of the genes in each signature; (e) comparing the output of the classifiers to pre-defined thresholds, cut-off values, or ranges of values that indicate likelihood of infection; (f) if the sample is negative for virus, repeating step (d) using only the bacteria classifier and non-infectious classifier; and (g) classifying the sample as being of bacterial etiology or noninfectious illness.
  • Another aspect of the present disclosure provides a method for determining whether an acute respiratory infection (ARI) in a subject is bacterial in origin, viral in origin, or non-infectious in origin comprising, consisting of, or consisting essentially of: (a) obtaining a biological sample from the subject; (b) determining the gene expression profile of the subject from the biological sample by evaluating the expression levels of pre-defined sets of genes; (c) normalizing gene expression levels for the technology (i.e., platform) used to make said measurement to generate a normalized value; (d) entering the normalized values into classifiers that have pre-defined weighting values for each of the genes in each signature; (e) comparing the output of the classifiers to pre-defined thresholds, cut-off values, or ranges of values that indicate likelihood of infection; (f) if the sample is negative for non-infectious illness, repeating step (d) using only the viral classifier and bacterial classifier; and (g) classifying the sample as being of viral etiology or bacterial et
  • Yet another aspect of the present disclosure provides a method of treating an acute respiratory infection (ARI) whose etiology is unknown in a subject, said method comprising, consisting of, or consisting essentially of: (a) obtaining a biological sample from the subject; (b) determining the gene expression profile of the subject from the biological sample by evaluating the expression levels of pre-defined sets of genes (e.g., one, two or three or more signatures); (c) normalizing gene expression levels for the technology (i.e., platform) used to make said measurement to generate a normalized value; (d) entering the normalized values into a bacterial classifier, a viral classifier and non-infectious illness classifier that have pre-defined weighting values for each of the genes in each signature; (e) comparing the output of the classifiers to pre-defined thresholds, cut-off values, or ranges of values that indicate likelihood of infection; (f) classifying the sample as being of bacterial etiology, viral etiology, or noninfectious illness;
  • step (g) comprises administering an antibacterial therapy when the etiology of the ARI is determined to be bacterial. In other embodiments, step (g) comprises administering an antiviral therapy when the etiology of the ARI is determined to be viral.
  • Another aspect is a method of monitoring response to a vaccine or a drug in a subject suffering from or at risk of an acute respiratory illness selected from bacterial, viral and/or non-infectious, comprising determining a host response of said subject, said determining carried out by a method as taught herein.
  • the drug is an antibacterial drug or an antiviral drug.
  • the methods further comprise generating a report assigning the subject a score indicating the probability of the etiology of the ARI.
  • a system for determining an etiology of an acute respiratory illness in a subject selected from bacterial, viral and/or non-infectious comprising one or more of (inclusive of combinations thereof): at least one processor; a sample input circuit configured to receive a biological sample from the subject; a sample analysis circuit coupled to the at least one processor and configured to determine gene expression levels of the biological sample; an input/output circuit coupled to the at least one processor; a storage circuit coupled to the at least one processor and configured to store data, parameters, and/or classifiers; and a memory coupled to the processor and comprising computer readable program code embodied in the memory that when executed by the at least one processor causes the at least one processor to perform operations comprising: controlling/performing measurement via the sample analysis circuit of gene expression levels of a pre-defined set of genes (i.e., signature) in said biological sample; normalizing the gene expression levels to generate normalized gene expression values; retrieving from the storage circuit a bacterial acute respiratory infection (ARI) classifier, a viral ARI class
  • the system comprises computer readable code to transform quantitative, or semi-quantitative, detection of gene expression to a cumulative score or probability of the etiology of the ARI.
  • the system comprises an array platform, a thermal cycler platform (e.g., multiplexed and/or real-time PCR platform), a hybridization and multi-signal coded (e.g., fluorescence) detector platform, a nucleic acid mass spectrometry platform, a nucleic acid sequencing platform, or a combination thereof.
  • a thermal cycler platform e.g., multiplexed and/or real-time PCR platform
  • a hybridization and multi-signal coded (e.g., fluorescence) detector platform e.g., fluorescence detector platform
  • a nucleic acid mass spectrometry platform e.g., a nucleic acid sequencing platform, or a combination thereof.
  • the pre-defined sets of genes comprise at least three genetic signatures.
  • the biological sample comprises a sample selected from the group consisting of peripheral blood, sputum, nasopharyngeal swab, nasopharyngeal wash, bronchoalveolar lavage, endotracheal aspirate, and combinations thereof.
  • the bacterial classifier comprises expression levels of 5, 10, 20, 30 or 50, to 80, 100, 150 or 200 of the genes (measurable, e.g., with oligonucleotide probes homologous to said genes or gene transcripts) listed as part of a bacterial classifier in Table 1, Table 2, Table 9, Table 10 and/or Table 12.
  • the viral classifier comprises expression levels of 5, 10, 20, 30 or 50, to 80, 100, 150 or 200 of the genes (measurable, e.g., with oligonucleotide probes homologous to said genes or gene transcripts) listed as part of a viral classifier in Table 1, Table 2, Table 9, Table 10 and/or Table 12.
  • the non-infectious illness classifier comprises expression levels of 5, 10, 20, 30 or 50, to 80, 100, 150 or 200 of the genes (measurable, e.g., with oligonucleotide probes homologous to said genes or gene transcripts) listed as part of a non-infectious illness classifier in Table 1, Table 2, Table 9, Table 10 and/or Table 12.
  • a kit for determining the etiology of an acute respiratory infection (ARI) in a subject comprising, consisting of, or consisting essentially of (a) a means for extracting mRNA from a biological sample; (b) a means for generating one or more arrays consisting of a plurality of synthetic oligonucleotides with regions homologous to transcripts from of 5, 10, 20, 30 or 50, to 80, 100, 150 or 200 of the genes from Table 1, Table 2, Table 9, Table 10 and/or Table 12; and (c) instructions for use.
  • a means for extracting mRNA from a biological sample comprising, consisting of, or consisting essentially of (a) a means for extracting mRNA from a biological sample; (b) a means for generating one or more arrays consisting of a plurality of synthetic oligonucleotides with regions homologous to transcripts from of 5, 10, 20, 30 or 50, to 80, 100, 150 or 200 of the genes from Table 1, Table 2, Table 9, Table 10 and/or
  • kits for assessing the acute respiratory infection (ARI) classifier comprising, consisting of, or consisting essentially of: (a) generating one or more arrays consisting of a plurality of synthetic oligonucleotides with regions homologous to of 5, 10, 20, 30 or 50, to 80, 100, 150 or 200 of the genes from Table 1, Table 2, Table 9, Table 10 and/or Table 12; (b) adding to said array oligonucleotides with regions homologous to normalizing genes; (c) obtaining a biological sample from a subject suffering from an acute respiratory infection (ARI); (d) isolating RNA from said sample to create a transcriptome; (e) measuring said transcriptome on said array (e.g., by measuring fluorescence or electric current proportional to the level of gene expression, etc.); (f) normalizing the measurements of said transcriptome to the normalizing genes, electronically transferring normalized measurements to a computer to implement the classifier(s), (g) generating a report; and
  • the method further comprises externally validating an ARI classifier against a known dataset comprising at least two relevant clinical attributes.
  • the dataset is selected from the group consisting of GSE6269, GSE42026, GSE40396, GSE20346, GSE42834 and combinations thereof.
  • Yet another aspect of the present disclosure provides all that is disclosed and illustrated herein.
  • ARI classifier as taught herein in a method of treatment for acute respiratory infection (ARI) in a subject of unknown etiology.
  • FIG. 1 is a schematic showing a method of obtaining classifiers (training 10 ) according to some embodiments of the present disclosure, where each classifier is composed of a weighted sum of all or a subset of normalized gene expression levels. This weighted sum defines a probability that allows for a decision (classification), particularly when compared to a threshold value or a confidence interval.
  • the exact combination of genes, their weights and the threshold for each classifier obtained by the training are particular to a specific platform.
  • the classifier (or more precisely its components, namely weights and threshold or confidence interval (values)) go to a database. Weights with a nonzero value determine the subset of genes used by the classifier. Repeat to obtain all three classifiers (bacterial ARI, viral ARI and non-infectious ARI) within a specified platform matching the gene expression values.
  • FIG. 2 is a diagram showing an example of generating and/or using classifiers in accordance with some embodiments of the present disclosure.
  • FIG. 3 is a schematic showing a method of classification 20 of an etiology of acute respiratory symptoms suffered by a subject making use of classifiers according to some embodiments of the present disclosure.
  • FIG. 4 presents schematics showing the decision pattern for using secondary classification to determine the etiology of an ARI in a subject in accordance with some embodiments of the present disclosure.
  • FIG. 5 is a diagram of an example training method presented in Example 1.
  • a cohort of patients encompassing bacterial ARI, viral ARI, or non-infectious illness was used to develop classifiers of each condition.
  • This combined ARI classifier was validated using leave one out cross-validation and compared to three published classifiers of bacterial vs. viral infection.
  • the combined ARI classifier was also externally validated in six publically available datasets. In one experiment, healthy volunteers were included in the training set to determine their suitability as “no-infection” controls. All subsequent experiments were performed without the use of this healthy subject cohort.
  • FIG. 6 presents graphs showing the results of leave-one-out cross-validation of three classifiers (bacterial ARI, viral ARI and noninfectious illness) according an example training method presented in Example 1.
  • Each patient is assigned probabilities of having bacterial ARI (triangle), viral ARI (circle), and non-infectious illness (square).
  • Patients clinically adjudicated as having bacterial ARI, viral ARI, or non-infectious illness, are presented in the top, center, and bottom panels, respectively. Overall classification accuracy was 87%.
  • FIG. 7 is a graph showing the evaluation of healthy adults as a no-infection control, rather than an ill-but-uninfected control. This figure demonstrates the unexpected superiority of the use of ill-but-not infected subjects as the control.
  • FIG. 8 shows the positive and negative predictive values for A) Bacterial and B) Viral ARI classification as a function of prevalence.
  • FIG. 9 is a Venn diagram representing overlap in the Bacterial ARI, Viral ARI, and Non-infectious Illness Classifiers. There are 71 genes in the Bacterial ARI Classifier, 33 in the Viral
  • ARI Classifier and 26 in the Non-infectious Illness Classifier.
  • One gene overlaps between the Bacterial and Viral ARI Classifiers.
  • Five genes overlap between the Bacterial ARI and Non-infectious Illness Classifiers.
  • Four genes overlap between the Viral ARI and Non-infectious Illness Classifiers.
  • FIG. 10 is a graph showing Classifier performance in patients with co-infection by the identification of bacterial and viral pathogens.
  • Subjects 1-6 have a bacterial host response.
  • Subjects 7-9 have both bacterial and viral host responses which may indicate true co-infection.
  • Subjects 10-23 have a viral host response.
  • Subjects 24-25 do not have bacterial or viral host responses.
  • FIG. 11 is a block diagram of a classification system and/or computer program product that may be used in a platform.
  • a classification system and/or computer program product 1100 may include a processor subsystem 1140 , including one or more Central Processing Units (CPU) on which one or more operating systems and/or one or more applications run. While one processor 1140 is shown, it will be understood that multiple processors 1140 may be present, which may be either electrically interconnected or separate.
  • Processor(s) 1140 are configured to execute computer program code from memory devices, such as memory 1150 , to perform at least some of the operations and methods described herein.
  • the storage circuit 1170 may store databases which provide access to the data/parameters/classifiers used by the classification system 1110 such as the signatures, weights, thresholds, etc.
  • An input/output circuit 1160 may include displays and/or user input devices, such as keyboards, touch screens and/or pointing devices. Devices attached to the input/output circuit 1160 may be used to provide information to the processor 1140 by a user of the classification system 1100 . Devices attached to the input/output circuit 1160 may include networking or communication controllers, input devices (keyboard, a mouse, touch screen, etc.) and output devices (printer or display).
  • An optional update circuit 1180 may be included as an interface for providing updates to the classification system 1100 such as updates to the code executed by the processor 1140 that are stored in the memory 1150 and/or the storage circuit 1170 . Updates provided via the update circuit 1180 may also include updates to portions of the storage circuit 1170 related to a database and/or other data storage format which maintains information for the classification system 1100 , such as the signatures, weights, thresholds, etc.
  • the sample input circuit 1110 provides an interface for the classification system 1100 to receive biological samples to be analyzed.
  • the sample processing circuit 1120 may further process the biological sample within the classification system 1100 so as to prepare the biological sample for automated analysis.
  • Articles “a” and “an” are used herein to refer to one or to more than one (i.e., at least one) of the grammatical object of the article.
  • an element means at least one element and can include more than one element.
  • the present disclosure provides that alterations in gene, protein and metabolite expression in blood in response to pathogen exposure that causes acute respiratory infections can be used to identify and characterize the etiology of the ARI in a subject with a high degree of accuracy.
  • ARI refers to an infection, or an illness showing symptoms and/or physical findings consistent with an infection (e.g., symptoms such as coughing, wheezing, fever, sore throat, congestion; physical findings such as elevated heart rate, elevated breath rate, abnormal white blood cell count, low arterial carbon dioxide tension (PaCO 2 ), etc.), of the upper or lower respiratory tract, often due to a bacterial or viral pathogen, and characterized by rapid progression of symptoms over hours to days.
  • ARIs may primarily be of the upper respiratory tract (URIs), the lower respiratory tract (LRIs), or a combination of the two. ARIs may have systemic effects due to spread of the infection beyond the respiratory tract or due to collateral damage induced by the immune response.
  • An example of the former includes Staphylococcus aureus pneumonia that has spread to the blood stream and can result in secondary sites of infection, including endocarditis (infection of the heart valves), septic arthritis (joint infection), or osteomyelitis (bone infection).
  • An example of the latter includes influenza pneumonia leading to acute respiratory distress syndrome and respiratory failure.
  • signature refers to a set of biological analytes and the measurable quantities of said analytes whose particular combination signifies the presence or absence of the specified biological state. These signatures are discovered in a plurality of subjects with known status (e.g., with a confirmed respiratory bacterial infection, respiratory viral infection, or suffering from non-infectious illness), and are discriminative (individually or jointly) of one or more categories or outcomes of interest. These measurable analytes, also known as biological markers, can be (but are not limited to) gene expression levels, protein or peptide levels, or metabolite levels. See also US 2015/0227681 to Courchesne et al.; US 2016/0153993 to Eden et al.
  • the “signature” is a particular combination of genes whose expression levels, when incorporated into a classifier as taught herein, discriminate a condition such as a bacterial ARI, viral ARI or non-infectious illness. See, for example, Table 1, Table 2, Table 9, Table 10 and Table 12 hereinbelow.
  • the signature is agnostic to the species of respiratory virus or bacteria (i.e., while differentiating between virus or bacteria, it does not differentiate between particular genus or species of virus or bacteria) and/or agnostic to the particular cause of the non-infectious illness.
  • classifier and “predictor” are used interchangeably and refer to a mathematical function that uses the values of the signature (e.g., gene expression levels for a defined set of genes) and a pre-determined coefficient (or weight) for each signature component to generate scores for a given observation or individual patient for the purpose of assignment to a category.
  • the classifier may be linear and/or probabilistic.
  • a classifier is linear if scores are a function of summed signature values weighted by a set of coefficients.
  • a classifier is probabilistic if the function of signature values generates a probability, a value between 0 and 1.0 (or 0 and 100%) quantifying the likelihood that a subject or observation belongs to a particular category or will have a particular outcome, respectively.
  • Probit regression and logistic regression are examples of probabilistic linear classifiers that use probit and logistic link functions, respectively, to generate a probability.
  • a classifier as taught herein may be obtained by a procedure known as “training,” which makes use of a set of data containing observations with known category membership (e.g., bacterial ARI, viral ARI, and/or non-infection illness). See FIG. 1 .
  • training seeks to find the optimal coefficient (i.e., weight) for each component of a given signature (e.g., gene expression level components), as well as an optimal signature, where the optimal result is determined by the highest achievable classification accuracy.
  • Classification refers to a method of assigning a subject suffering from or at risk for acute respiratory symptoms to one or more categories or outcomes (e.g., a patient is infected with a pathogen or is not infected, another categorization may be that a patient is infected with a virus and/or infected with a bacterium). See FIG. 3 .
  • a subject may be classified to more than one category, e.g., in case of bacterial and viral co-infection.
  • the outcome, or category is determined by the value of the scores provided by the classifier, which may be compared to a cut-off or threshold value, confidence level, or limit. In other scenarios, the probability of belonging to a particular category may be given (e.g., if the classifier reports probabilities).
  • the term “indicative” when used with gene expression levels means that the gene expression levels are up-regulated or down-regulated, altered, or changed compared to the expression levels in alternative biological states (e.g., bacterial ARI or viral ARI) or control.
  • alternative biological states e.g., bacterial ARI or viral ARI
  • indicator when used with protein levels means that the protein levels are higher or lower, increased or decreased, altered, or changed compared to the standard protein levels or levels in alternative biological states.
  • subject and “patient” are used interchangeably and refer to any animal being examined, studied or treated. It is not intended that the present disclosure be limited to any particular type of subject.
  • humans are the preferred subject, while in other embodiments non-human animals are the preferred subject, including, but not limited to, mice, monkeys, ferrets, cattle, sheep, goats, pigs, chicken, turkeys, dogs, cats, horses and reptiles.
  • the subject is suffering from an ARI or is displaying ARI-like symptoms.
  • Platinum” or “technology” as used herein refers to an apparatus (e.g., instrument and associated parts, computer, computer-readable media comprising one or more databases as taught herein, reagents, etc.) that may be used to measure a signature, e.g., gene expression levels, in accordance with the present disclosure.
  • platforms include, but are not limited to, an array platform, a thermal cycler platform (e.g., multiplexed and/or real-time PCR platform), a nucleic acid sequencing platform, a hybridization and multi-signal coded (e.g., fluorescence) detector platform, etc., a nucleic acid mass spectrometry platform, a magnetic resonance platform, and combinations thereof.
  • the platform is configured to measure gene expression levels semi-quantitatively, that is, rather than measuring in discrete or absolute expression, the expression levels are measured as an estimate and/or relative to each other or a specified marker or markers (e.g., expression of another, “standard” or “reference,” gene).
  • semi-quantitative measuring includes “real-time PCR” by performing PCR cycles until a signal indicating the specified mRNA is detected, and using the number of PCR cycles needed until detection to provide the estimated or relative expression levels of the genes within the signature.
  • a real-time PCR platform includes, for example, a TaqMan® Low Density Array (TLDA), in which samples undergo multiplexed reverse transcription, followed by real-time PCR on an array card with a collection of wells in which real-time PCR is performed. See Kodani et al. 2011, J. Clin. Microbial. 49(6):2175-2182.
  • a real-time PCR platform also includes, for example, a Biocartis IdyllaTM sample-to-result technology, in which cells are lysed, DNA/RNA extracted and real-time PCR is performed and results detected.
  • a magnetic resonance platform includes, for example, T2 Biosystems® T2 Magnetic Resonance (T2MR®) technology, in which molecular targets may be identified in biological samples without the need for purification.
  • T2MR® T2 Magnetic Resonance
  • arrays are interchangeable and refer to an arrangement of a collection of nucleotide sequences presented on a substrate. Any type of array can be utilized in the methods provided herein.
  • arrays can be on a solid substrate (a solid phase array), such as a glass slide, or on a semi-solid substrate, such as nitrocellulose membrane.
  • Arrays can also be presented on beads, i.e., a bead array. These beads are typically microscopic and may be made of, e.g., polystyrene.
  • the array can also be presented on nanoparticles, which may be made of, e.g., particularly gold, but also silver, palladium, or platinum.
  • the nucleotide sequences can be DNA, RNA, or any permutations thereof (e.g., nucleotide analogues, such as locked nucleic acids (LNAs), and the like). In some embodiments, the nucleotide sequences span exon/intron boundaries to detect gene expression of spliced or mature RNA species rather than genomic DNA.
  • the nucleotide sequences can also be partial sequences from a gene, primers, whole gene sequences, non-coding sequences, coding sequences, published sequences, known sequences, or novel sequences.
  • the arrays may additionally comprise other compounds, such as antibodies, peptides, proteins, tissues, cells, chemicals, carbohydrates, and the like that specifically bind proteins or metabolites.
  • An array platform includes, for example, the TaqMan® Low Density Array (TLDA) mentioned above, and an Affymetrix® microarray platform.
  • TLDA TaqMan® Low Density Array
  • a hybridization and multi-signal coded detector platform includes, for example, NanoString nCounter® technology, in which hybridization of a color-coded barcode attached to a target-specific probe (e.g., corresponding to a gene expression transcript of interest) is detected; and Luminex® xMAP® technology, in which microsphere beads are color coded and coated with a target-specific (e.g., gene expression transcript) probe for detection; and Illumina® BeadArray, in which microbeads are assembled onto fiber optic bundles or planar silica slides and coated with a target-specific (e.g., gene expression transcript) probe for detection.
  • NanoString nCounter® technology in which hybridization of a color-coded barcode attached to a target-specific probe (e.g., corresponding to a gene expression transcript of interest) is detected
  • Luminex® xMAP® technology in which microsphere beads are color coded and coated with a target-specific (e.g., gene expression transcript) probe for detection
  • a nucleic acid mass spectrometry platform includes, for example, the Ibis Biosciences Plex-ID® Detector, in which DNA mass spectrometry is used to detect amplified DNA using mass profiles.
  • a thermal cycler platform includes, for example, the FilmArray® multiplex PCR system, which extract and purifies nucleic acids from an unprocessed sample and performs nested multiplex PCR; and the RainDrop Digital PCR System, which is a droplet-based PCR platform using microfluidic chips.
  • computer readable medium refers to any device or system for storing and providing information (e.g., data and instructions) to a computer processor.
  • Examples of computer readable media include, but are not limited to, DVDs, CDs hard disk drives, magnetic tape and servers for streaming media over networks, and applications, such as those found on smart phones and tablets.
  • aspects of the present invention including data structures and methods may be stored on a computer readable medium. Processing and data may also be performed on numerous device types, including but not limited to, desk top and lap top computers, tablets, smart phones, and the like.
  • a biological sample comprises any sample that may be taken from a subject that contains genetic material that can be used in the methods provided herein.
  • a biological sample may comprise a peripheral blood sample.
  • peripheral blood sample refers to a sample of blood circulating in the circulatory system or body taken from the system of body.
  • Other samples may comprise those taken from the upper respiratory tract, including but not limited to, sputum, nasopharyngeal swab and nasopharyngeal wash.
  • a biological sample may also comprise those samples taken from the lower respiratory tract, including but not limited to, bronchoalveolar lavage and endotracheal aspirate.
  • a biological sample may also comprise any combinations thereof.
  • genetic material refers to a material used to store genetic information in the nuclei or mitochondria of an organism's cells.
  • examples of genetic material include, but are not limited to, double-stranded and single-stranded DNA, cDNA, RNA, and mRNA.
  • plurality of nucleic acid oligomers refers to two or more nucleic acid oligomers, which can be DNA or RNA.
  • treat refers to the reduction or amelioration of the severity, duration and/or progression of a disease or disorder or one or more symptoms thereof resulting from the administration of one or more therapies. Such terms refer to a reduction in the replication of a virus or bacteria, or a reduction in the spread of a virus or bacteria to other organs or tissues in a subject or to other subjects. Treatment may also include therapies for ARIs resulting from non-infectious illness, such as allergy treatment, asthma treatments, and the like.
  • the term “effective amount” refers to an amount of a therapeutic agent that is sufficient to exert a physiological effect in the subject.
  • response refers to a change in gene expression levels of genes in a subject in response to the subject being infected with a virus or bacteria or suffering from a non-infectious illness compared to the gene expression levels of the genes in a subject that is not infected with a virus, bacteria or suffering from a non-infectious illness or a control subject.
  • a therapeutic agent for treating a subject having bacteremia is an antibiotic which include, but are not limited to, penicillins, cephalosporins, fluroquinolones, tetracyclines, macrolides, and aminoglycosides.
  • a therapeutic agent for treating a subject having a viral respiratory infection includes, but is not limited to, oseltamivir, RNAi antivirals, inhaled ribavirin, monoclonal antibody respigam, zanamivir, and neuraminidase blocking agents.
  • the invention contemplates the use of the methods of the invention to determine treatments with antivirals or antibiotics that are not yet available.
  • Appropriate treatment regimes also include treatments for ARIs resulting from non-infectious illness, such as allergy treatments, including but not limited to, administration of antihistamines, decongestants, anticholinergic nasal sprays, leukotriene inhibitors, mast cell inhibitors, steroid nasal sprays, etc.; and asthma treatments, including, but not limited to, inhaled corticosteroids, leukotriene modifiers, long-acting beta agonists, combinations inhalers (e.g., fluticasone-salmeterol; budesonide-formoterol; mometasone-formoterol, etc.), theophylline, short-acting beta agonists, ipratropium, oral and intravenous corticosteroids, omalizumab, and the like.
  • allergy treatments including but not limited to, administration of antihistamines, decongestants, anticholinergic nasal sprays, leukotriene inhibitors, mast cell inhibitors
  • Such regimens require the act of administering to a subject a therapeutic agent(s) capable of producing reduction of symptoms associated with a disease state.
  • therapeutic agents include, but are not limited to, NSAIDS, acetaminophen, anti-histamines, beta-agonists, anti-tussives or other medicaments that reduce the symptoms associated with the disease process.
  • the present disclosure provides methods of generating classifiers (also referred to as training 10 ) for use in the methods of determining the etiology of an acute respiratory illness in a subject.
  • Gene expression-based classifiers are developed that can be used to identify and characterize the etiology of an ARI in a subject with a high degree of accuracy.
  • an acute respiratory infection (ARI) classifier comprising, consisting of, or consisting essentially of: (i) obtaining a biological sample (e.g., a peripheral blood sample) from a plurality of subjects suffering from bacterial, viral or non-infectious acute respiratory infection 100 ; (ii) optionally, isolating RNA from said sample (e.g., total RNA to create a transcriptome) ( 105 , not shown in FIG.
  • a biological sample e.g., a peripheral blood sample
  • RNA from said sample e.g., total RNA to create a transcriptome
  • the sample is not purified after collection.
  • the sample may be purified to remove extraneous material, before or after lysis of cells.
  • the sample is purified with cell lysis and removal of cellular materials, isolation of nucleic acids, and/or reduction of abundant transcripts such as globin or ribosomal RNAs.
  • measuring gene expression levels may include generating one or more microarrays using said transcriptomes; measuring said transcriptomes using a plurality of primers; analyzing and correcting batch differences.
  • the method further includes uploading 140 the final gene target list for the generated classifier, the associated weights (w n ), and threshold values to one or more databases.
  • FIG. 2 An example of generating said classifiers is detailed in FIG. 2 .
  • biological samples from a cohort of patients encompassing bacterial ARI, viral ARI, or non-infectious illness are used to develop gene expression-based classifiers for each condition (i.e., bacterial acute respiratory infection, viral acute respiratory infection, or non-infectious cause of illness).
  • the bacterial ARI classifier is obtained to positively identifying those with bacterial ARI vs. either viral ARI or non-infectious illnesses.
  • the viral ARI classifier is obtained to positively identifying those with viral ARI vs. bacterial ARI or non-infectious illness (NI).
  • the non-infectious illness classifier is generated to improve bacterial and viral ARI classifier specificity.
  • signatures for bacterial ARI classifiers, viral ARI classifiers, and non-infectious illness classifiers are generated (e.g., by applying a sparse logistic regression model).
  • the ARI classifier may then be combined, if desired, into a single classifier termed “the ARI classifier” by following a one-versus-all scheme whereby largest membership probability assigns class label. See also FIG. 5 .
  • the combined ARI classifier may be validated in some embodiments using leave-one-out cross-validation in the same population from which it was derived and/or may be validated in some embodiments using publically available human gene expression datasets of samples from subjects suffering from illness of known etiology.
  • validation may be performed using publically available human gene expression datasets (e.g., GSE6269, GSE42026, GSE40396, GSE20346, and/or GSE42834), the datasets chosen if they included at least two clinical groups (bacterial ARI, viral ARI, or non-infectious illness).
  • GSE6269 e.g., GSE6269, GSE42026, GSE40396, GSE20346, and/or GSE42834
  • the datasets chosen if they included at least two clinical groups bacterial ARI, viral ARI, or non-infectious illness.
  • the classifier may be validated in a standard set of samples from subjects suffering from illness of known etiology, i.e., bacterial ARI, viral ARI, or non-infectious illness.
  • the methods and assays of the present disclosure may be based upon gene expression, for example, through direct measurement of RNA, measurement of derived materials (e.g., cDNA), and measurement of RNA products (e.g., encoded proteins or peptides). Any method of extracting and screening gene expression may be used and is within the scope of the present disclosure.
  • the measuring comprises the detection and quantification (e.g., semi-quantification) of mRNA in the sample.
  • the gene expression levels are adjusted relative to one or more standard gene level(s) (“normalized”). As known in the art, normalizing is done to remove technical variability inherent to a platform to give a quantity or relative quantity (e.g., of expressed genes).
  • detection and quantification of mRNA may first involve a reverse transcription and/or amplification step, e.g., RT-PCR such as quantitative RT-PCR.
  • detection and quantification may be based upon the unamplified mRNA molecules present in or purified from the biological sample.
  • Direct detection and measurement of RNA molecules typically involves hybridization to complementary primers and/or labeled probes.
  • Such methods include traditional northern blotting and surface-enhanced Raman spectroscopy (SERS), which involves shooting a laser at a sample exposed to surfaces of plasmonic-active metal structures with gene-specific probes, and measuring changes in light frequency as it scatters.
  • SERS surface-enhanced Raman spectroscopy
  • RNA derivatives typically involves hybridization to complementary primers and/or labeled probes.
  • This may include high-density oligonucleotide probe arrays (e.g., solid state microarrays and bead arrays) or related probe-hybridization methods, and polymerase chain reaction (PCR)-based amplification and detection, including real-time, digital, and end-point PCR methods for relative and absolute quantitation of specific RNA molecules.
  • PCR polymerase chain reaction
  • sequencing-based methods can be used to detect and quantify RNA or
  • RNA-derived material levels When applied to RNA, sequencing methods are referred to as RNAseq, and provide both qualitative (sequence, or presence/absence of an RNA, or its cognate cDNA, in a sample) and quantitative (copy number) information on RNA molecules from a sample. See, e.g., Wang et al. 2009 Nat. Rev. Genet. 10(1):57-63.
  • Another sequence-based method serial analysis of gene expression (SAGE), uses cDNA “tags” as a proxy to measure expression levels of RNA molecules.
  • mRNA detection and quantification may also be used to complete the methods of the present disclosure.
  • PixelTM System incorporating Molecular IndexingTM, developed by CELLULAR RESEARCH, INC., NanoString® Technologies nCounter gene expression system
  • mRNA-Seq Molecular Indexing
  • Tag-Profiling Tag-Profiling
  • BeadArrayTM technology VeraCode from Illumina
  • VeraCode VeraCode from Illumina
  • ICEPlex System from PrimeraDx
  • QuantiGene 2.0 Multiplex Assay from Affymetrix.
  • RNA from whole blood from a subject can be collected using RNA preservation reagents such as PAXgeneTM RNA tubes (PreAnalytiX, Valencia, Calif.).
  • the RNA can be extracted using a standard PAXgeneTM or VersageneTM (Gentra Systems, Inc, Minneapolis, Minn.) RNA extraction protocol.
  • the VersageneTM kit produces greater yields of higher quality RNA from the PAXgeneTM RNA tubes.
  • GLOBINCIearTM Ambion, Austin, Tex.
  • RNA quality can be assessed using an Agilent 2100 Bioanalyzer immediately following extraction. This analysis provides an RNA Integrity Number (RIN) as a quantitative measure of RNA quality. Also, following globin reduction the samples can be compared to the globin-reduced standards. In addition, the scaling factors and background can be assessed following hybridization to microarrays.
  • RIN RNA Integrity Number
  • Real-time PCR may be used to quickly identify gene expression from a whole blood sample.
  • the isolated RNA can be reverse transcribed and then amplified and detected in real time using non-specific fluorescent dyes that intercalate with the resulting ds-DNA, or sequence-specific DNA probes labeled with a fluorescent reporter which permits detection only after hybridization of the probe with its complementary DNA target.
  • the expression levels are typically normalized following detection and quantification as appropriate for the particular platform using methods routinely practiced by those of ordinary skill in the art.
  • the individual categories of classifiers are formed from a cohort inclusive of a variety of such causes thereof.
  • the bacterial ARI classifier is obtained from a cohort having bacterial infections from multiple bacterial genera and/or species
  • the viral ARI classifier is obtained from a cohort having viral infections from multiple viral genera and/or species
  • the non-infectious illness classifier is obtained from a cohort having a non-infectious illness due to multiple non-infectious causes. See, e.g., Table 8.
  • the respective classifiers obtained are agnostic to the underlying bacteria, virus, and non-infectious cause.
  • some or all of the subjects with non-infectious causes of illness in the cohort have symptoms consistent with a respiratory infection.
  • the signatures may be obtained using a supervised statistical approach known as sparse linear classification in which sets of genes are identified by the model according to their ability to separate phenotypes during a training process that uses the selected set of patient samples.
  • the outcomes of training are gene signatures and classification coefficients for the three comparisons. Together the signatures and coefficients provide a classifier or predictor.
  • Training may also be used to establish threshold or cut-off values. Threshold or cut-off values can be adjusted to change test performance, e.g., test sensitivity and specificity. For example, the threshold for bacterial ARI may be intentionally lowered to increase the sensitivity of the test for bacterial infection, if desired.
  • the classifier generating comprises iteratively: (i) assigning a weight for each normalized gene expression value, entering the weight and expression value for each gene into a classifier (e.g., a linear regression classifier) equation and determining a score for outcome for each of the plurality of subjects, then (ii) determining the accuracy of classification for each outcome across the plurality of subjects, and then (iii) adjusting the weight until accuracy of classification is optimized.
  • a classifier e.g., a linear regression classifier
  • the classifier is a linear regression classifier and said generating comprises converting a score of said classifier to a probability using a link function.
  • the link function specifies the link between the target/output of the model (e.g., probability of bacterial infection) and systematic components (in this instance, the combination of explanatory variables that comprise the predictor) of the linear model. It says how the expected value of the response relates to the linear predictor of explanatory variable.
  • the present disclosure further provides methods for determining whether a patient has a respiratory illness due to a bacterial infection, a viral infection, or a non-infectious cause.
  • the method for making this determination relies upon the use of classifiers obtained as taught herein.
  • the methods may include: a) measuring the expression levels of pre-defined sets of genes (i.e., for one or more of the three signatures); b) normalizing gene expression levels for the technology used to make said measurement; c) taking those values and entering them into a bacterial classifier, a viral classifier and/or non-infectious illness classifier (i.e., predictors) that have pre-defined weighting values (coefficients) for each of the genes in each signature; d) comparing the output of the classifiers to pre-defined thresholds, cut-off values, confidence intervals or ranges of values that indicate likelihood of infection; and optionally e) jointly reporting the results of the classifiers.
  • each of the three gene signatures is informative of the patient's host response to a different ARI etiology (bacterial or viral) or to an ill, but not infected, state (NI).
  • ARI etiology bacterial or viral
  • NI ill, but not infected, state
  • These signatures are groups of gene transcripts which have consistent and coordinated increased or decreased levels of expression in response to one of three clinical states: bacterial ARI, viral ARI, or a non-infected but ill state.
  • These signatures are derived using carefully adjudicated groups of patient samples with the condition(s) of interest (training 10 ).
  • the mRNA is extracted.
  • the mRNA (or a defined region of each mRNA), is quantified for all, or a subset, of the genes in the signatures.
  • the mRNA may have to be first purified from the sample.
  • the signature is reflective of a clinical state and is defined relative to at least one of the other two possibilities.
  • the bacterial ARI signature is identified as a group of biomarkers (here, represented by gene mRNA transcripts) that distinguish patients with bacterial ARI and those without bacterial ARI (including patients with viral ARI or non-infectious illness as it pertains to this application).
  • the viral ARI signature is defined by a group of biomarkers that distinguish patients with viral ARI from those without viral ARI (including patients with either bacterial ARI or non-infectious illness).
  • the non-infectious illness signature is defined by a group of biomarkers that distinguish patients with non-infectious causes of illness relative to those with either bacterial or viral ARI.
  • the normalized expression levels of each gene of the signature are the explanatory or independent variables or features used in the classifier.
  • the classifier may have a general form as a probit regression formulation:
  • ⁇ ( ⁇ ) is the probit (or logistic, etc.) link function
  • ⁇ 1 , ⁇ 2 , . . . , ⁇ d ⁇ are the coefficients obtained during training (e.g., second, third and fourth columns from Table 9) (coefficients may also be denoted ⁇ w 1 ,w 2 , . . . , w d ⁇ as “weights” herein);
  • ⁇ X 1 ,X 2 , . . . , X d ⁇ are the normalized gene expression levels of the signature; and d is the size of the signature (i.e., number of genes).
  • the value of the coefficients for each explanatory variable will change for each technology platform used to measure the expression of the genes or a subset of genes used in the probit regression model.
  • the coefficients for each of the features in the classifier algorithm are shown in Table 9.
  • the sensitivity, specificity, and overall accuracy of each classifier may be optimized by changing the threshold for classification using receiving operating characteristic (ROC) curves.
  • ROC operating characteristic
  • Another aspect of the present disclosure provides a method for determining whether an acute respiratory infection (ARI) in a subject is bacterial in origin, viral in origin, or non-infectious in origin comprising, consisting of, or consisting essentially of a) obtaining a biological sample from the subject; b) determining the gene expression profile of the subject from the biological sample by evaluating the expression levels of pre-defined sets of genes (i.e., three signatures); c) normalizing gene expression levels for the technology used to make said measurement to generate a normalized value; d) entering the normalized value into a bacterial classifier, a viral classifier and non-infectious illness classifier (i.e., predictors) that have pre-defined weighting values (coefficients) for each of the genes in each signature; e) comparing the output of the classifiers to pre-defined thresholds, cut-off values, or ranges of values that indicate likelihood of infection; and e) classifying the sample as being of bacterial etiology, viral etiology
  • the classifiers that are developed during training and using a training set of samples are applied for prediction purposes to diagnose new individuals (“classification”). For each subject or patient, a biological sample is taken and the normalized levels of expression (i.e., the relative amount of mRNA expression) in the sample of each of the genes specified by the signatures found during training are the input for the classifiers. The classifiers also use the weighting coefficients discovered during training for each gene. As outputs, the classifiers are used to compute three probability values. Each probability value may be used to determine the likelihood of the three considered clinical states: bacterial ARI, viral ARI, and non-infectious illness.
  • the results of each of the classifiers are reported.
  • the three signatures with their corresponding coefficients are applied to an individual patient to obtain three probability values, namely probability of having a bacterial ARI, viral ARI, and a non-infectious illness. In some embodiments, these values may be reported relative to a reference range that indicates the confidence with which the classification is made.
  • the output of the classifier may be compared to a threshold value, for example, to report a “positive” in the case that the classifier score or probability exceeds the threshold indicating the presence of one or more of a bacterial ARI, viral ARI, or non-infectious illness. If the classifier score or probability fails to reach the threshold, the result would be reported as “negative” for the respective condition.
  • the values for bacterial and viral ARI alone are reported and the report is silent on the likelihood of ill but not infected.
  • a classifier obtained with one platform may not show optimal performance on another platform. This could be due to the promiscuity of probes or other technical issues particular to the platform. Accordingly, also described herein are methods to adapt a signature as taught herein from one platform for another.
  • a signature obtained from an Affymetrix platform may be adapted to a TLDA platform by the use of corresponding TLDA probes for the genes in the signature and/or substitute genes correlated with those in the signature, for the Affymetrix platform.
  • Table 1 shows a list of Affymetrix probes and the genes they measure, plus “replacement genes” that are introduced as replacements for gene probes that either may not perform well on the TLDA platform for technical reasons or to replace those Affymetrix probes for which there is no cognate TLDA probe. These replacements may indicate highly correlated genes or may be probes that bind to a different location in the same gene transcript. Additional genes may be included, such as pan-viral gene probes.
  • the weights shown in Table 1 are weights calculated for a classifier implemented on the microarray platform. Weights that have not been estimated are indicated by “NA” in the table. (Example 4 below provides the completed translation of these classifiers to the TLDA platform.)
  • Reference probes for TLDA i.e., normalization genes, e.g., TRAP1, PPIB, GAPDH and 18S
  • TLDA normalization genes, e.g., TRAP1, PPIB, GAPDH and 18S
  • Affymetrix probeset ID are not part of the classifier.
  • Additional gene probes that do not necessarily correspond to the Affymetrix probeset also have “NA” in the Affymetrix probeset ID column.
  • This method of determining the etiology of an ARI may be combined with other tests. For example, if the patient is determined to have a viral ARI, a follow-up test may be to determine if influenza A or B can be directly detected or if a host response indicative of such an infection can be detected. Similarly, a follow-up test to a result of bacterial ARI may be to determine if a Gram positive or a Gram negative bacterium can be directly detected or if a host response indicative of such an infection can be detected. In some embodiments, simultaneous testing may be performed to determine the class of infection using the classifiers, and also to test for specific pathogens using pathogen-specific probes or detection methods. See, e.g., US 2015/0284780 to Eley et al. (method for detecting active tuberculosis); US 2014/0323391 to Tsalik et al. (method for classification of bacterial infection).
  • an acute respiratory infection (ARI) in a subject is bacterial in origin, viral in origin, or non-infectious in origin
  • a method for determining whether an acute respiratory infection (ARI) in a subject is bacterial in origin, viral in origin, or non-infectious in origin comprising, consisting of, or consisting essentially of (a) obtaining a biological sample from the subject; (b) determining the gene expression profile of the subject from the biological sample by evaluating the expression levels of pre-defined sets of genes (i.e., three signatures); (c) normalizing gene expression levels as required for the technology used to make said measurement to generate a normalized value; (d) entering the normalized value into classifiers (i.e., predictors) that have pre-defined weighting values (coefficients) for each of the genes in each signature; (e) comparing the output of the classifiers to pre-defined thresholds, cut-off values, or ranges of values that indicate likelihood of infection; (f) if the sample is negative for bacteria, repeating step (d
  • Another aspect of the present provides a method for determining whether an acute respiratory infection (ARI) in a subject is bacterial in origin, viral in origin, or non-infectious in origin comprising, consisting of, or consisting essentially of (a) obtaining a biological sample from the subject; (b) determining the gene expression profile of the subject from the biological sample by evaluating the expression levels of pre-defined sets of genes (i.e., three signatures); (c) normalizing gene expression levels for the technology used to make said measurement to generate a normalized value; (d) entering the normalized value into classifiers (i.e., predictors) that have pre-defined weighting values (coefficients) for each of the genes in each signature; (e) comparing the output of the classifiers to pre-defined thresholds, cut-off values, or ranges of values that indicate likelihood of infection; (f) if the sample is negative for virus, repeating step (d) using only the bacteria classifier and non-infectious classifier; and (g) classifying the sample as being of
  • Yet another aspect of the present provides a method for determining whether an acute respiratory infection (ARI) in a subject is bacterial in origin, viral in origin, or non-infectious in origin comprising, consisting of, or consisting essentially of (a) obtaining a biological sample from the subject; (b) determining the gene expression profile of the subject from the biological sample by evaluating the expression levels of pre-defined sets of genes (i.e., three signatures); (c) normalizing gene expression levels for the technology used to make said measurement to generate a normalized value; (d) entering the normalized value into classifiers (i.e., predictors) that have pre-defined weighting values (coefficients) for each of the genes in each signature; (e) comparing the output of the classifiers to pre-defined thresholds, cut-off values, or ranges of values that indicate likelihood of infection; (f) if the sample is negative for non-infectious illness, repeating step (d) using only the viral classifier and bacterial classifier; and (g) classifying the sample
  • the method further comprises generating a report assigning the patient a score indicating the probability of the etiology of the ARI.
  • Classifying the status of a patient using a secondary classification scheme is shown in FIG. 4 .
  • the bacterial ARI classifier will distinguish between patients with a bacterial ARI from those without a bacterial ARI, which could, instead, be a viral ARI or a non-infectious cause of illness.
  • a secondary classification can then be imposed on those patients with non-bacterial ARI to further discriminate between viral ARI and non-infectious illness.
  • This same process of primary and secondary classification can also be applied to the viral ARI classifier where patients determined not to have a viral infection would then be secondarily classified as having a bacterial ARI or non-infectious cause of illness.
  • applying the non-infectious illness classifier as a primary test will determine whether patients have such a non-infectious illness or instead have an infectious cause of symptoms.
  • the secondary classification step would determine if that infectious is due to bacterial or viral pathogens.
  • Results from the three primary and three secondary classifications can be summed through various techniques by those skilled in the art (such as summation, counts, or average) to produce an actionable report for the provider.
  • the genes used for this secondary level of classification can be some or all of those presented in Table 2.
  • the three classifiers described above are used to perform the 1 st level classification. Then for those patients with non-bacterial infection, a secondary classifier is defined to distinguish viral ARI from those with non-infectious illness ( FIG. 4 , left panel). Similarly, for those patients with non-viral infection, a new classifier is used to distinguish viral from non-infectious illness ( FIG. 4 , middle panel), and for those patients who are not classified as having a non-infectious illness in the first step, a new classifier is used to distinguish between viral and bacterial ARI ( FIG. 4 , right panel).
  • nine probabilities may be generated, and those probabilities may be combined in a number of ways.
  • Two strategies are described here as a way to reconcile the three sets of predictions, where each has a probability of bacterial ARI, viral ARI, and non-infectious illness.
  • Highest predicted average probability All predicted probabilities for bacterial ARI are averaged, as are all the predicted probabilities of viral ARI and, similarly, all predicted probabilities of non-infectious illness. The greatest averaged probability denotes the diagnosis.
  • the Result of Tier 1 classification could be, for example (clinical classification presented in rows; diagnostic test prediction presented in columns) similar to that presented in Table 3.
  • Classification can be achieved, for example, as described above, and/or as summarized in Table 2.
  • Table 2 summarizes the gene membership in three distinct classification strategies that solve different diagnostic questions. There are a total of 270 probes that collectively comprise three complex classifiers. The first is referred to as BVS (Bacterial ARI, Viral ARI, SIRS), which is the same as that presented below in Example 1. These probes are the same as those presented in Table 9, which offers probe/gene weights used in classification. They also correspond to the genes presented in Table 10.
  • the second is referred to as 2L for 2-layer or 2-tier. This is the hierarchical scheme presented in FIG. 4 .
  • the third is a one-tier classification scheme, BVSH, which is similar to BVS but also includes a population of healthy controls (similarly described in Example 1).
  • This group has been shown to be a poor control for non-infection, but there are use cases in which discrimination from healthy may be clinically important. For example, this can include the serial measurement of signatures to correlate with convalescence. It may also be used to discriminate patients who have been exposed to an infectious agent and are presymptomatic vs. asymptomatic.
  • four groups are represented in the training cohort—those with bacterial ARI, viral ARI, SIRS (non-infectious illness), and Healthy. These four groups are used to generate four distinct signatures that distinguish each class from all other possibilities.
  • BVSH refers to a one-level classification scheme that includes healthy individuals in the training cohort and therefore includes a classifier for the healthy state as compared to bacterial ARI, viral ARI, or non-infectious illness.
  • the dark grey BVSH column identifies any gene or probe included in this classification scheme.
  • This scheme is itself comprised by BVSH-BO, BVSH-VO, BVSH-SO, and BVSH-HO with their respective probe/gene compositions denoted by ‘1’ in these columns.
  • Table 2 provides a summary of use of members of the gene sets for viral, bacterial, and non-infectious illness classifiers that are constructed according to the required task.
  • a ‘1’ indicates membership of the gene in the classifier.
  • Another aspect of the present disclosure provides a method of treating an acute respiratory infection (ARI) whose etiology is unknown in a subject, said method comprising, consisting of, or consisting essentially of (a) obtaining a biological sample from the subject; (b) determining the gene expression profile of the subject from the biological sample by evaluating the expression levels of pre-defined sets of genes (e.g., one, two or three or more signatures); (c) normalizing gene expression levels as required for the technology used to make said measurement to generate a normalized value; (d) entering the normalized value into a bacterial classifier, a viral classifier and non-infectious illness classifier (i.e., predictors) that have pre-defined weighting values (coefficients) for each of the genes in each signature; (e) comparing the output of the classifiers to pre-defined thresholds, cut-off values, or ranges of values that indicate likelihood of infection; (f) classifying the sample as being of bacterial etiology, viral etiology,
  • step (g) comprises administering an antibacterial therapy when the etiology of the ARI is determined to be bacterial. In other embodiments, step (g) comprises administering an antiviral therapy when the etiology of the ARI is determined to be viral.
  • ARI etiology of the ARI of the subject
  • she may undergo treatment, for example anti-viral therapy if the ARI is determined to be viral, and/or she may be quarantined to her home for the course of the infection.
  • bacterial therapy regimens may be administered (e.g., administration of antibiotics) if the ARI is determined to be bacterial.
  • Those subjects classified as non-infectious illness may be sent home or seen for further diagnosis and treatment (e.g., allergy, asthma, etc.).
  • the person performing the peripheral blood sample need not perform the comparison, however, as it is contemplated that a laboratory may communicate the gene expression levels of the classifiers to a medical practitioner for the purpose of identifying the etiology of the ARI and for the administration of appropriate treatment. Additionally, it is contemplated that a medical professional, after examining a patient, would order an agent to obtain a peripheral blood sample, have the sample assayed for the classifiers, and have the agent report patient's etiological status to the medical professional. Once the medical professional has obtained the etiology of the ARI, the medical professional could order suitable treatment and/or quarantine.
  • the methods provided herein can be effectively used to diagnose the etiology of illness in order to correctly treat the patient and reduce inappropriate use of antibiotics. Further, the methods provided herein have a variety of other uses, including but not limited to, (1) a host-based test to detect individuals who have been exposed to a pathogen and have impending, but not symptomatic, illness (e.g., in scenarios of natural spread of diseases through a population but also in the case of bioterrorism); (2) a host-based test for monitoring response to a vaccine or a drug, either in a clinical trial setting or for population monitoring of immunity; (3) a host-based test for screening for impending illness prior to deployment (e.g., a military deployment or on a civilian scenario such as embarkation on a cruise ship); and (4) a host-based test for the screening of livestock for ARIs (e.g., avian flu and other potentially pandemic viruses).
  • a host-based test to detect individuals who have been exposed to a pathogen and have impending, but not symptomatic,
  • kits for determining the etiology of an acute respiratory infection (ARI) in a subject comprising, consisting of, or consisting essentially of (a) a means for extracting a biological sample; (b) a means for generating one or more arrays consisting of a plurality of synthetic oligonucleotides with regions homologous to a group of gene transcripts as taught herein; and (c) instructions for use.
  • Yet another aspect of the present disclosure provides a method of using a kit for assessing the acute respiratory infection (ARI) classifier comprising, consisting of, or consisting essentially of: (a) generating one or more arrays consisting of a plurality of synthetic oligonucleotides with regions homologous to a a group of gene transcripts as taught herein; (b) adding to said array oligonucleotides with regions homologous to normalizing genes; (c) obtaining a biological sample from a subject suffering from an acute respiratory infection (ARI); (d) isolating RNA from said sample to create a transcriptome; (e) measuring said transcriptome on said array; (f) normalizing the measurements of said transcriptome to the normalizing genes, electronically transferring normalized measurements to a computer to implement the classifier algorithm(s), (g) generating a report; and optionally (h) administering an appropriate treatment based on the results.
  • ARI acute respiratory infection
  • a classification system and/or computer program product 1100 may be used in or by a platform, according to various embodiments described herein.
  • a classification system and/or computer program product 1100 may be embodied as one or more enterprise, application, personal, pervasive and/or embedded computer systems that are operable to receive, transmit, process and store data using any suitable combination of software, firmware and/or hardware and that may be standalone and/or interconnected by any conventional, public and/or private, real and/or virtual, wired and/or wireless network including all or a portion of the global communication network known as the Internet, and may include various types of tangible, non-transitory computer readable medium.
  • the classification system 1100 may include a processor subsystem 1140 , including one or more Central Processing Units (CPU) on which one or more operating systems and/or one or more applications run. While one processor 1140 is shown, it will be understood that multiple processors 1140 may be present, which may be either electrically interconnected or separate. Processor(s) 1140 are configured to execute computer program code from memory devices, such as memory 1150 , to perform at least some of the operations and methods described herein, and may be any conventional or special purpose processor, including, but not limited to, digital signal processor (DSP), field programmable gate array (FPGA), application specific integrated circuit (ASIC), and multi-core processors.
  • DSP digital signal processor
  • FPGA field programmable gate array
  • ASIC application specific integrated circuit
  • the memory subsystem 1150 may include a hierarchy of memory devices such as Random Access Memory (RAM), Read-Only Memory (ROM), Erasable Programmable Read-Only Memory (EPROM) or flash memory, and/or any other solid state memory devices.
  • RAM Random Access Memory
  • ROM Read-Only Memory
  • EPROM Erasable Programmable Read-Only Memory
  • flash memory any other solid state memory devices.
  • a storage circuit 1170 may also be provided, which may include, for example, a portable computer diskette, a hard disk, a portable Compact Disk Read-Only Memory (CDROM), an optical storage device, a magnetic storage device and/or any other kind of disk- or tape-based storage subsystem.
  • the storage circuit 1170 may provide non-volatile storage of data/parameters/classifiers for the classification system 1100 .
  • the storage circuit 1170 may include disk drive and/or network store components.
  • the storage circuit 1170 may be used to store code to be executed and/or data to be accessed by the processor 1140 .
  • the storage circuit 1170 may store databases which provide access to the data/parameters/classifiers used for the classification system 1110 such as the signatures, weights, thresholds, etc. Any combination of one or more computer readable media may be utilized by the storage circuit 1170 .
  • the computer readable media may be a computer readable signal medium or a computer readable storage medium.
  • a computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing.
  • a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
  • An input/output circuit 1160 may include displays and/or user input devices, such as keyboards, touch screens and/or pointing devices. Devices attached to the input/output circuit 1160 may be used to provide information to the processor 1140 by a user of the classification system 1100 . Devices attached to the input/output circuit 1160 may include networking or communication controllers, input devices (keyboard, a mouse, touch screen, etc.) and output devices (printer or display). The input/output circuit 1160 may also provide an interface to devices, such as a display and/or printer, to which results of the operations of the classification system 1100 can be communicated so as to be provided to the user of the classification system 1100 .
  • An optional update circuit 1180 may be included as an interface for providing updates to the classification system 1100 . Updates may include updates to the code executed by the processor 1140 that are stored in the memory 1150 and/or the storage circuit 1170 . Updates provided via the update circuit 1180 may also include updates to portions of the storage circuit 1170 related to a database and/or other data storage format which maintains information for the classification system 1100 , such as the signatures, weights, thresholds, etc.
  • the sample input circuit 1110 of the classification system 1100 may provide an interface for the platform as described hereinabove to receive biological samples to be analyzed.
  • the sample input circuit 1110 may include mechanical elements, as well as electrical elements, which receive a biological sample provided by a user to the classification system 1100 and transport the biological sample within the classification system 1100 and/or platform to be processed.
  • the sample input circuit 1110 may include a bar code reader that identifies a bar-coded container for identification of the sample and/or test order form.
  • the sample processing circuit 1120 may further process the biological sample within the classification system 1100 and/or platform so as to prepare the biological sample for automated analysis.
  • the sample analysis circuit 1130 may automatically analyze the processed biological sample.
  • the sample analysis circuit 1130 may be used in measuring, e.g., gene expression levels of a pre-defined set of genes with the biological sample provided to the classification system 1100 .
  • the sample analysis circuit 1130 may also generate normalized gene expression values by normalizing the gene expression levels.
  • the sample analysis circuit 1130 may retrieve from the storage circuit 1170 a bacterial acute respiratory infection (ARI) classifier, a viral ARI classifier and a non-infectious illness classifier, these classifier(s) comprising pre-defined weighting values (i.e., coefficients) for each of the genes of the pre-defined set of genes.
  • ARI bacterial acute respiratory infection
  • the sample analysis circuit 1130 may enter the normalized gene expression values into one or more acute respiratory illness classifiers selected from the bacterial acute respiratory infection (ARI) classifier, the viral ARI classifier and the non-infectious illness classifier.
  • the sample analysis circuit 1130 may calculate an etiology probability for one or more of a bacterial ARI, viral ARI and non-infectious illness based upon said classifier(s) and control output, via the input/output circuit 1160 , of a determination whether the acute respiratory illness in the subject is bacterial in origin, viral in origin, non-infectious in origin, or some combination thereof.
  • the sample input circuit 1110 , the sample processing circuit 1120 , the sample analysis circuit 1130 , the input/output circuit 1160 , the storage circuit 1170 , and/or the update circuit 1180 may execute at least partially under the control of the one or more processors 1140 of the classification system 1100 .
  • executing “under the control” of the processor 1140 means that the operations performed by the sample input circuit 1110 , the sample processing circuit 1120 , the sample analysis circuit 1130 , the input/output circuit 1160 , the storage circuit 1170 , and/or the update circuit 1180 may be at least partially executed and/or directed by the processor 1140 , but does not preclude at least a portion of the operations of those components being separately electrically or mechanically automated.
  • the processor 1140 may control the operations of the classification system 1100 , as described herein, via the execution of computer program code.
  • Computer program code for carrying out operations for aspects of the present disclosure may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C++, C#, VB.NET, Python or the like, conventional procedural programming languages, such as the “C” programming language, Visual Basic, Fortran 2003, Perl, COBOL 2002, PHP, ABAP, dynamic programming languages such as Python, Ruby and Groovy, or other programming languages.
  • the program code may execute entirely on the classification system 1100 , partly on the classification system 1100 , as a stand-alone software package, partly on the classification system 1100 and partly on a remote computer or entirely on the remote computer or server.
  • the remote computer may be connected to the classification system 1100 through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider) or in a cloud computer environment or offered as a service such as a Software as a Service (SaaS).
  • LAN local area network
  • WAN wide area network
  • SaaS Software as a Service
  • the system includes computer readable code that can transform quantitative, or semi-quantitative, detection of gene expression to a cumulative score or probability of the etiology of the ARI.
  • the system is a sample-to-result system, with the components integrated such that a user can simply insert a biological sample to be tested, and some time later (preferably a short amount of time, e.g., 30 or 45 minutes, or 1, 2, or 3 hours, up to 8, 12, 24 or 48 hours) receive a result output from the system.
  • some time later preferably a short amount of time, e.g., 30 or 45 minutes, or 1, 2, or 3 hours, up to 8, 12, 24 or 48 hours
  • any numerical range recited herein includes all values from the lower value to the upper value. For example, if a concentration range is stated as 1% to 50%, it is intended that values such as 2% to 40%, 10% to 30%, or 1% to 3%, etc., are expressly enumerated in this specification. These are only examples of what is specifically intended, and all possible combinations of numerical values between and including the lowest value and the highest value enumerated are to be considered to be expressly stated in this application.
  • Acute respiratory infections due to bacterial or viral pathogens are among the most common reasons for seeking medical care.
  • Current pathogen-based diagnostic approaches are not reliable or timely, thus most patients receive inappropriate antibiotics.
  • Host response biomarkers offer an alternative diagnostic approach to direct antimicrobial use.
  • the samples that formed the basis for discovery were drawn from an observational, cohort study conducted at four tertiary care hospital emergency departments and a student health facility. 44 healthy controls and 273 patients with community-onset acute respiratory infection or non-infectious illness were selected from a larger cohort of patients with suspected sepsis (CAPSOD study). Mean age was 45 years and 45% of participants were male. Further demographic information may be found in Table 1 of Tsalik et al. (2016) Sci Transl Med 9(322):1-9, which is incorporated by reference herein.
  • Clinical phenotypes were adjudicated through manual chart review. Routine microbiological testing and multiplex PCR for respiratory viral pathogens were performed. Peripheral whole blood gene expression was measured using microarrays. Sparse logistic regression was used to develop classifiers of bacterial vs. viral vs. non-infectious illness. Five independently derived datasets including 328 individuals were used for validation.
  • Gene expression-based classifiers were developed for bacterial acute respiratory infection (71 probes), viral acute respiratory infection (33 probes), or a non-infectious cause of illness (26 probes). The three classifiers were applied to 273 patients where class assignment was determined by the highest predicted probability. Overall accuracy was 87% (23 8/273 concordant with clinical adjudication), which was more accurate than procalcitonin (78%, p ⁇ 0.03) and three published classifiers of bacterial vs. viral infection (78-83%). The classifiers developed here externally validated in five publicly available datasets (AUC 0.90-0.99). We compared the classification accuracy of the host gene expression-based tests to procalcitonin and clinically adjudicated diagnoses, which included bacterial or viral acute respiratory infection or non-infectious illness.
  • the host's peripheral blood gene expression response to infection offers a diagnostic strategy complementary to those already in use. 8 This strategy has successfully characterized the host response to viral 8-13 and bacterial ARI 11,14 . Despite these advances, several issues preclude their use as diagnostics in patient care settings. An important consideration in the development of host-based molecular signatures is that they be developed in the intended use population. 15 However, nearly all published gene expression-based ARI classifiers used healthy individuals as controls and focused on small or homogeneous populations and are thus not optimized for use in acute care settings where patients present with undifferentiated symptoms. Furthermore, the statistical methods used to identify gene-expression classifiers often include redundant genes based on clustering, univariate testing, or pathway association. These strategies identify relevant biology but do not maximize diagnostic performance. An alternative, as exemplified here, is to combine genes from unrelated pathways to generate a more informative classifier.
  • ARI cases included patients with upper or lower respiratory tract symptoms, as adjudicated by emergency medicine (SWG, EBQ) or infectious diseases (ELT) physicians.
  • Adjudications were based on retrospective, manual chart reviews performed at least 28 days after enrollment and prior to any gene expression-based categorization, using previously published criteria. 17 The totality of information used to support these adjudications would not have been available to clinicians at the time of their evaluation. Seventy patients with microbiologically confirmed bacterial ARI were identified including four with pharyngitis and 66 with pneumonia.
  • This panel detects influenza A and B, adenovirus (B, E), parainfluenza 1-4, respiratory syncytial virus A and B, human metapneumovirus, human rhinovirus, coronavirus (229E, OC43, NL63, HKU1), coxsackie/echo virus, and bocavirus.
  • the transcriptomes of 317 subjects (273 ill patients and 44 healthy volunteers) were measured in two microarray batches with seven overlapping samples (GSE63990). Exploratory principal component analysis and hierarchical clustering revealed substantial batch differences. These were corrected by first estimating and removing probe-wise mean batch effects using the Bayesian fixed effects model. Next, we fitted a robust linear regression model with Huber loss function using seven overlapping samples, which was used to adjust the remaining expression values.
  • Sparse classification methods such as sparse logistic regression perform classification and variable selection simultaneously while reducing over-fitting risk. 21 Therefore, separate gene selection strategies such as univariate testing or sparse factor models are unnecessary.
  • a sparse logistic regression model was fitted independently to each of the binary tasks using the 40% of probes with the largest variance after batch correction. 22 Specifically, we used a Lasso regularized generalized linear model with binomial likelihood with nested cross-validation to select for the regularization parameters. Code was written in Matlab using the Glmnet toolbox. This generated Bacterial ARI, Viral ARI, and Non-Infectious Illness classifiers. Provided that each binary classifier estimates class membership probabilities (e.g., probability of bacterial vs.
  • Classification performance metrics included area-under-the-receiving-operating-characteristic-curve (AUC) for binary outcomes and confusion matrices for ternary outcomes.
  • the ARI classifier was validated using leave-one-out cross-validation in the same population from which it was derived. Independent, external validation occurred using publically available human gene expression datasets from 328 individuals (GSE6269, GSE42026, GSE40396, GSE20346, and GSE42834). Datasets were chosen if they included at least two clinical groups (bacterial ARI, viral ARI, or non-infectious illness). To match probes across different microarray platforms, each ARI classifier probe was converted to gene symbols, which were used to identify corresponding target microarray probes.
  • Clinical decision making is infrequently binary, requiring the simultaneous distinction of multiple diagnostic possibilities.
  • the assigned probability represents the extent to which the patient's gene expression response matches that condition's canonical signature. Since each signature intentionally functions independently of the others, the probabilities are not expected to sum to one. To simplify classification, the highest predicted probability determined class assignment. Overall classification accuracy was 87% (238/273 were concordant with adjudicated phenotype).
  • Bacterial ARI was identified in 58/70 (83%) patients and excluded 179/191 (94%) without bacterial infection. Viral ARI was identified in 90% (104/115) and excluded in 92% (145/158) of cases. Using the non-infectious illness classifier, infection was excluded in 86% of cases (76/88). Sensitivity analyses was performed for positive and negative predictive values for all three classifiers given that prevalence can vary for numerous reasons including infection type, patient characteristics, or location ( FIG. 8 ). For both bacterial and viral classification, predictive values remained high across a range of prevalence estimates, including those typically found for ARI.
  • Procalcitonin a widely used biomarker specific for bacterial infection.
  • Procalcitonin concentrations were determined for the 238 subjects where samples were available and compared to ARI classifier performance for this subgroup.
  • Procalcitonin correctly classified 186 of 238 patients (78%) compared to 204/238 (86%) using the ARI classifier (p 0.03).
  • accuracy for the two strategies varied depending on the classification task. For example, performance was similar in discriminating viral from bacterial ARI.
  • Generating classifiers from high dimensional, gene expression data can result in over-fitting.
  • the Viral classifier included known anti-viral response categories such as interferon response, T-cell signaling, and RNA processing.
  • the Viral classifier had the greatest representation of RNA processing pathways such as KPNB1, which is involved in nuclear transport and is co-opted by viruses for transport of viral proteins and genomes. 26,27 Its downregulation suggests it may play an antiviral role in the host response.
  • the Bacterial classifier encompassed the greatest breadth of cellular processes, notably cell cycle regulation, cell growth, and differentiation.
  • the Bacterial classifier included genes important in T-, B-, and NK-cell signaling.
  • Unique to the Bacterial classifier were genes involved in oxidative stress, and fatty acid and amino acid metabolism, consistent with sepsis-related metabolic perturbations. 28
  • Including patients with bacterial and viral infections allows for the distinction between these two states but does not address how to classify non-infectious illness.
  • This phenotype is important to include because patients present with infectious and non-infectious etiologies that may share symptoms. That is, symptoms may not provide a clinician with a high degree of diagnostic certainty.
  • the current approach which uniquely appreciates the necessity of including the three most likely states for ARI symptoms, can be applied to an undifferentiated clinical population where such a test is in greatest need.
  • the small number of discordant classifications occurred may have arisen either from errors in classification or clinical phenotyping. Errors in clinical phenotyping can arise from a failure to identify causative pathogens due to limitations in current microbiological diagnostics. Alternatively, some non-infectious disease processes may in fact be infection-related through mechanisms that have yet to be discovered. Discordant cases were not clearly explained by a unifying variable such as pathogen type, syndrome, or patient characteristic.
  • the gene expression classifiers presented herein may be impacted by other factors including patient-specific variables (e.g., treatment, comorbidity, duration of illness); test-specific variables (e.g., sample processing, assay conditions, RNA quality and yield); or as-of-yet unidentified variables.
  • patient-specific variables e.g., treatment, comorbidity, duration of illness
  • test-specific variables e.g., sample processing, assay conditions, RNA quality and yield
  • as-of-yet unidentified variables e.g., unidentified variables.
  • the co-identification group was defined by the presence of both bacterial and viral pathogens without further subcategorization as to the likelihood of bacterial or viral disease.
  • FIG. 10 is an informative representation of infection status, which could be used by a clinician to diagnose the etiology of ARI.
  • Mycoplasma pneumoniae 1 Pasteurella multocida 1 Polymicrobial 11 Pantoea sp.; Coagulase negative Staphylococcus 1 Pseudomonas aeruginosa ; Alcaligenes xylosoxidans 1 Pseudomonas aeruginosa ; Serratia marcescens 1 Staphylococcus aureus ; Haemophilus influenzae 2 Staphylococcus aureus ; Proteus mirabilis 1 Staphylococcus aureus ; Viridans Group Streptococcus; 1 Escherichia coli Streptococcus pneumoniae ; Haemophilus sp.
  • Probes selected for the Bacterial ARI, Viral ARI, and Non-infectious Illness Classifiers are presented as Affymetrix probe IDs. Values for each probe represent the weight of each probe in the specified classifier.
  • RT-PCR quantitative real-time PCR
  • TLDA TaqMan Low Density Array
  • TLDA cards will be manufactured with one or more TaqMan primer/probe sets specific for a gene mRNA transcript in the classifier(s) in each well, along with multiple endogenous control RNA targets (primer/probe sets) for data normalization.
  • purified total RNA is reverse transcribed into cDNA, loaded into a master well and distributed into each assay well via centrifugation through microfluidic channels.
  • TaqMan hydrolysis probes rely on 5′ to 3′ exonuclease activity to cleave the dual-labeled probe during hybridization to complementary target sequence with each amplification round, resulting in fluorescent signal production. In this manner, quantitative detection of the accumulated PCR products in “real-time” is possible. During exponential amplification and detection, the number of PCR cycles at which the fluorescent signal exceeds a detection threshold is the threshold cycle (C t ) or quantification cycle (C q )—as determined by commercial software for the RT-PCR instrument.
  • C t threshold cycle
  • C q quantification cycle
  • the C t for a target RNA is subtracted from the C t of endogenous normalization RNA (or the geometric mean of multiple normalization RNAs), providing a deltaC t value for each RNA target within a sample which indicates relative expression of a target RNA normalized for variability in amount or quality of input sample RNA or cDNA.
  • the data for the quantified gene signatures are then processed using a computer and according to the probit classifier described above (equation 1) and reproduce here.
  • Normalized gene expression levels of each gene of the signature are the explanatory or independent variables or features used in the classifier, in this example the general form of the classifier is a probit regression formulation:
  • ⁇ ( ⁇ ) is the probit link function
  • ⁇ 1 , ⁇ 2 , . . . , ⁇ d ⁇ are the coefficients obtained during training
  • ⁇ X 1 ,X 2 , . . . , X d ⁇ are the normalized genes expression values of the signature
  • d is the size of the signature (number of genes).
  • the value of the coefficients for each explanatory variable are specific to the technology platform used to measure the expression of the genes or a subset of genes used in the probit regression model.
  • the computer program computes a score, or probability, and compares the score to a threshold value.
  • the sensitivity, specificity, and overall accuracy of each classifier is optimized by changing the threshold for classification using receiving operating characteristic (ROC) curves.
  • ROC operating characteristic
  • the genes of the three signatures that compose the Host Response-ARI (HR-ARI) test were transitioned to a Custom TaqMan® Low Density Array Cards from ThermoFisher Scientific (Waltham, Mass.). Expression of these gene signatures were measured using custom multianalyte quantitative real time PCR (RT-qPCR) assays on the 384-well TaqMan Low Density Array (TLDA; Thermo-Fisher) platform.
  • HR-ARI Host Response-ARI
  • TLDA cards were designed and manufactured with one or more TaqMan primer/probe sets per well, each representing a specific RNA transcript in the ARI signatures, along with multiple endogenous control RNA targets (TRAP1, PPIB, GAPDH, FPGS, DECR1 and 18S) that are used to normalize for RNA loading and to control for plate-to-plate variability.
  • endogenous control RNA targets (TRAP1, PPIB, GAPDH, FPGS, DECR1 and 18S) that are used to normalize for RNA loading and to control for plate-to-plate variability.
  • two reference genes out of five available
  • primer/probe sets with more than 33% missing values (below limits of quantification) were discarded.
  • the remaining missing values are set to 1+max(Cq), where Cq is the quantification cycle for RT-qPCR. Normalized expression values were then calculated as the average of the selected references minus the observed Cq values for any given primer/probe set. See Hellemans et al. (2007) Geno
  • 144 primer/probe sets measure gene targets representative of the 132 previously described Affymetrix (microarray) probes of the three ARI gene signatures (i.e., the genes in the bacterial gene expression signature, the viral gene expression signature and the non-infectious gene expression signature); 6 probe sets are for reference genes, and we additionally assayed 24 probe sets from a previously-discovered pan-viral gene signature. See U.S. Pat. No. 8,821,876; Zaas et al. Cell Host Microbe (2009) 6(3):207-217.
  • RNA was purified from PAXgene Blood RNA tubes (PreAnalytix) and reverse transcribed into cDNA using the Superscript VILO cDNA synthesis kit (Thermo-Fisher) according to the manufacturer's recommended protocol.
  • a standard amount of cDNA for each sample was loaded per master well, and distributed into each TaqMan assay well via centrifugation through microfluidic channels.
  • the TaqMan hydrolysis probes rely on 5′ to 3′ exonuclease activity to cleave the dual-labeled probe during hybridization to complementary target sequence with each amplification round, resulting in fluorescent signal production.
  • Quantitative detection of the fluorescence indicates accumulated PCR products in “real-time.”
  • the number of PCR cycles at which the fluorescent signal exceeds a detection threshold is the threshold cycle (C t ) or quantification cycle (C q )—as determined by commercial software for the RT-qPCR instrument.
  • Non-infectious illness was defined by the presence of SIRS criteria, which includes at least two of the following four features; Temperature ⁇ 36° or >38° C.; Heart rate >90 beats per minute; Respiratory rate >20 breaths per minute or arterial partial pressure of CO 2 ⁇ 32 mmHg; and white blood cell count ⁇ 4000 or >12,000 cells/mm 3 or >10% band form neutrophils.
  • SIRS criteria includes at least two of the following four features; Temperature ⁇ 36° or >38° C.; Heart rate >90 beats per minute; Respiratory rate >20 breaths per minute or arterial partial pressure of CO 2 ⁇ 32 mmHg; and white blood cell count ⁇ 4000 or >12,000 cells/mm 3 or >10% band form neutrophils.
  • Three subjects did not report ethnicity. M, Male. F, Female. B, Black. W, White, O, Other/Unknown.
  • GSE numbers refer to NCBI Gene Expression Omnibus datasets. N/A, Not available based on published data.
  • bacterial ARI vs. viral ARI and non-infectious illness
  • viral ARI vs. bacterial ARI and non-infectious illness
  • non-infectious illness vs. bacterial and viral ARI.
  • the thresholds for each of the classifiers are selected from Receiving Operating Characteristic (ROC) curves using a symmetric cost function (expected sensitivity and specificity are approximately equal) (Fawcett (2006) Pattern Recogn Lett 27:861-874).
  • a subject is predicted as bacterial ARI if p(bacterial ARI)>t b , where t b is the threshold for the bacterial ARI classifier.
  • t b is the threshold for the viral ARI classifier.
  • t v and t n are the thresholds for the viral ARI and non-infectious illness classifiers.
  • a combined prediction can be made by selecting the most likely condition, i.e., the one with largest probability, specifically we write, argmax ⁇ p(bacterial ARI),p(viral ARI),p(non-infectious illness) ⁇ .
  • the model yielded bacterial ARI, viral ARI, and non-infectious illness accuracies of 80% (24 correct of 30), 77.4% (24 correct of 31) and 85.3% (29 correct of 34), respectively.
  • area under the ROC curves 0.92, 0.86 and 0.91, for the bacterial ARI, viral ARI and non-infectious illness classifier, respectively.
  • the weights and thresholds for each of the classifiers are shown in the Table 12, shown below. Note that this Table lists 151 gene targets instead of 174 gene targets because the reference genes were removed in the preprocessing stage, as described above, as were 17 targets for which there were missing values. These 17 targets were also removed during the preprocessing stage.
  • panviral signature genes are removed, we see a slight decreased performance, no larger than 5% across AUC, accuracies and percent of agreement values.
  • the composite host-response ARI classifier is composed of gene expression signatures that are diagnostic of bacterial ARI versus viral ARI, versus non-infectious illness and a mathematical classification framework.
  • the mathematical classifiers provide three discrete probabilities: that a subject has a bacterial ARI, viral ARI, or non-infectious illness. In each case, a cutoff or threshold may be specified above which threshold one would determine that a patient has the condition. In addition, one may modify the threshold to alter the sensitive and specificity of the test.
  • the measurement of these gene expression levels can occur on a variety of technical platforms.
  • the mathematical framework that determines ARI etiology probabilities is adapted to the platform by platform-specific training to accommodate transcript measurement methods (i.e., establishing platform-specific weights, w 1 , . . . , w p ). Similar, straightforward, methodology could be conducted to translate the gene signatures to other gene expression detection platforms, and then train the associated classifiers.
  • This Example also demonstrates good concordance between TLDA-based and microarray-based classification of etiology of ARI.
  • TLDA Assay ID Bacterial Viral Non-infectious Group Gene Symbol RefSeq ID Gene Name Hs00153304_m1 0.44206 ⁇ 0.19499 0 CD44 NM_000610.3; NM_001202555.1; hCG1811182 Celera Annotation; CD44 molecule (Indian blood NM_001001392.1; NM_001202556.1; group) NM_001001391.1; NM_001001390.1; NM_001001389.1 Hs00155778_m1 0 0 0 APLP2 NM_001142278.1; NM_001142277.1; hCG2032871 Celera Annotation; amyloid beta (A4) precursor-like NM_001142276.1; NR_024515.1; protein 2 NR_024516.1; NM_001642.2; NM_

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WO2021252864A1 (en) * 2020-06-11 2021-12-16 Duke University Methods to detect and treat sars-cov-2 (covid19) infection
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WO2023091970A1 (en) * 2021-11-16 2023-05-25 The General Hospital Corporation Live-cell label-free prediction of single-cell omics profiles by microscopy

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